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Modeling peak ground acceleration for earthquake hazard safety evaluation.

Fatima Khalid1, Milad Razbin2

  • 1Department of Civil Engineering, NED University of Engineering and Technology, Karachi, Pakistan. fatimak@neduet.edu.pk.

Scientific Reports
|December 27, 2024
PubMed
Summary

This study introduces an artificial neural network (ANN) model, enhanced with a genetic algorithm (GA), for predicting seismic ground motion (GMP). The ANN-GA model accurately forecasts earthquake occurrences, improving seismic hazard safety evaluations for infrastructure.

Keywords:
Artificial neural networkEarthquakeGenetic algorithmPeak ground acceleration

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Area of Science:

  • Geophysics
  • Seismology
  • Computational Science

Background:

  • Accurate ground motion prediction (GMP) is vital for seismic hazard assessment.
  • Existing models require enhancement for improved earthquake forecasting.
  • Shallow earthquakes pose significant risks to infrastructure and urban planning.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) model for predicting peak ground acceleration (PGA).
  • To integrate a genetic algorithm (GA) with the ANN to improve predictive accuracy.
  • To enhance seismic hazard safety evaluations through advanced earthquake forecasting.

Main Methods:

  • Utilized an artificial neural network (ANN) architecture with specific input (moment magnitude, fault type, distance, soil type) and output (PGA) variables.
  • Trained and validated the ANN model using 885 data pairs from the Pacific Engineering Research Center.
  • Integrated a genetic algorithm (GA) to optimize the ANN and forecast 20 potential earthquake scenarios.

Main Results:

  • The ANN model architecture included 4 input nodes, 2 hidden layers with 25 nodes each, and 1 output node.
  • The ANN-GA model successfully predicted earthquake occurrences in 15 out of 20 tested scenarios.
  • The model demonstrated significant potential in accurately forecasting seismic events.

Conclusions:

  • The developed ANN-GA model shows strong potential for accurate seismic event forecasting.
  • This predictive capability can significantly contribute to resilient infrastructure development.
  • Findings support better-informed urban planning strategies for earthquake-prone regions.